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River Flow Modelling Using Fuzzy Decision Trees

Author

Listed:
  • D. Han
  • I. Cluckie
  • D. Karbassioun
  • J. Lawry
  • B. Krauskopf

Abstract

A modern real time flood forecasting system requires itsmathematical model(s) to handle highly complex rainfall runoffprocesses. Uncertainty in real time flood forecasting willinvolve a variety of components such as measurement noise fromtelemetry systems, inadequacy of the models, insufficiency ofcatchment conditions, etc. Probabilistic forecasting is becomingmore and more important in this field. This article describes a novel attempt to use a Fuzzy Logic approach for river flow modelling based on fuzzy decision trees. These trees are learntfrom data using the MA-ID3 algorithm. This is an extension of Quinlan's ID3 and is based on mass assignments. MA-ID3 allows for the incorporation of fuzzy attribute and class values intodecision trees aiding generalisation and providing a framework for representing linguistic rules. The article showed that with only five fuzzy labels, the FDT model performed reasonably welland a comparison with a Neural Network model (Back Propagation)was carried out. Furthermore, the FDT model indicated that therainfall values of four or five days before the prediction time are regarded as more informative to the prediction than the morerecent ones. Although its performance is not as good as the neural network model in the test case, its glass box nature couldprovide some useful insight about the hydrological processes. Copyright Kluwer Academic Publishers 2002

Suggested Citation

  • D. Han & I. Cluckie & D. Karbassioun & J. Lawry & B. Krauskopf, 2002. "River Flow Modelling Using Fuzzy Decision Trees," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(6), pages 431-445, December.
  • Handle: RePEc:spr:waterr:v:16:y:2002:i:6:p:431-445
    DOI: 10.1023/A:1022251422280
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    References listed on IDEAS

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    1. D. Panigrahi & P. Mujumdar, 2000. "Reservoir Operation Modelling with Fuzzy Logic," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 14(2), pages 89-109, April.
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    Cited by:

    1. M. G. Erechtchoukova & P. A. Khaiter & S. Saffarpour, 2016. "Short-Term Predictions of Hydrological Events on an Urbanized Watershed Using Supervised Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4329-4343, September.
    2. François Colin & Serge Guillaume & Bruno Tisseyre, 2011. "Small Catchment Agricultural Management Using Decision Variables Defined at Catchment Scale and a Fuzzy Rule-Based System: A Mediterranean Vineyard Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2649-2668, September.
    3. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.

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